Title：Perishable Resource Allocation with Online Contextual Learning
Presenter：Associate professor Jie Song,Peking University
Time：4.00 p.m. December 19, 2019
Venue: Room 401,Economic and Trade Building
Host: Professor Erbao Cao
Sponsor: School of Economics＆Trade, Hunan University
Abstrct：We formulate a novel class of online matching problems with learning. In these problems, randomly arriving customers must be matched to perishable resources so as to maximize a total expected reward. The matching accounts for variations in rewards among different customer-resource pairings. It also accounts for the perishability of the resources. For concreteness, we focus on healthcare platforms, but our work can be easily extended to other service applications. Our work belongs to the online resource allocation streams in service system. We propose the first online algorithm for contextual learning and resource allocation with perishable resources. Our algorithm explores and exploits in distinct interweaving phases. We prove that our algorithm achieve an expected regret per period of O(K−1/3),where K is the number of planning cycles. We propose a pioneer algorithm that helps service system to optimize resource allocation decisions while learns the uncertain reward of matching customer-resource pairings.